An analysis of the effect of social norms on payment of speeding fines

ABSTRACT

Fines are the most common form of formal speed limit enforcement applied in jurisdictions across the world. From a deterrence perspective, fines deter speeding through high risk of detection and swift enforcement of financial penalties. Yet, evidence on factors associated with fine payment compliance is limited. In this study, we consider the extent to which elements of deterrence and social behavioural norms influence the dynamics of speeding fine payment. We merge lifetime driving and fine payment records from over 10,000 drivers who received a speeding infringement notice during a one-week period in 2017 across communities in Queensland, Australia with community level socio-demographic data. We combine these data with our indicator of community level fine payment norms; the proportion of residents with an outstanding fine debt and conduct survival analysis to estimate the impact of social norms on days taken to pay speeding fines after controlling for community wealth and driving history. We find that individuals living in communities where a greater proportion of residents have outstanding fine debt take longer to pay fines after controlling for community disadvantage and social disorganisation. Our findings support the social norms perspective that speeding fine compliance is enhanced by normative community behaviours.

KEYWORDS:

• Road safety policy
• traffic infringements
• social norms
• deterrence
• speeding

Acknowledgements

The authors acknowledge former Assistant Commissioner of the Queensland Police Service. The views expressed in this material are those of the authors and are not those of the Queensland Police Service. Responsibility for any errors of omission or commission remains with the authors. The Queensland Police Service expressly disclaims any liability for any damage resulting from the use of the material contained in this publication and will not be responsible for any loss, howsoever arising, from use or reliance on this material.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to confidentiality and ethical agreements, access to the individual driver data will require independent agreements be negotiated with the original data custodians. The authors can make community level data and modelling information available on request.

Notes

1 These systems involve loss of points according to the severity of the driver’s incursion, with suspension of driver’s license when all points have been lost (Pulido et al., 2010).

2 Suburbs are administrative units defined under the non-ABS structures of the Australian Statistical Geography Standard (ASGS). Geographical units defined by the ASGS are used as they provide a framework of statistical geography which enables the production of statistics which are comparable and can be spatially integrated. As of the 2016 census, Queensland comprised 3,263 suburbs. The average size of Queensland suburbs is 529.23 square kilometres. The average population is 1441. We refer to suburbs as communities or neighbourhoods throughout.

3 Results for the interaction models can be sourced from the authors.

Additional information

Funding

This evaluation was funded in part by the Queensland Police Service.